Name
Abstract | ||
|---|---|---|
To solve the efficiency problems associated with huge amounts of GIS data that are stored and accessed with a common relational database, the authors propose the following solution: the storage method of a non-relational database. On the basis of GIS data features: volume of data, variety of data types, and complexity of data structures, the authors analyzed the application requirements of the large GIS data storage and processing, and checked the big data support from the NoSQL document database of mongoDB. The authors propose an efficiently large data storage method for GIS spatial data in NoSQL (Not only SQL) database. Finally, by means of the installation and configuration of mongoDB server, the authors have also carried out parsing, storage, and query of the huge GIS spatial data of the shape file format by using the Python programming language under the Linux platform. The results show that the NoSQL database has obvious advantages by comparison to the traditional relational databases. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/GEOINFORMATICS.2014.6950846 | Geoinformatics |
Keywords | Field | DocType |
shape file format,relational databases,gis spatial data parsing,big data,mongodb server configuration,relational database,geographic information systems,data structure complexity,gis spatial data query,nosql,gis data access,linux platform,data volume,nosql document database,python programming language,nonrelational database,gis spatial data,linux,large gis data processing,gis spatial data storage,mongodb,large gis data storage,object-oriented programming,mongodb server installation,python,data type variety,query processing,sql,not only sql database,object oriented programming,shape,hardware | Data mining,Database model,Computer science,XML database,View,AM/FM/GIS,Database design,NoSQL,Enterprise GIS,Database,Spatial database | Conference |
ISSN | Citations | PageRank |
2161-024X | 1 | 0.37 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaomin Zhang | 1 | 1 | 0.37 |
| Wei Song | 2 | 1 | 0.37 |
| Liming Liu | 3 | 1 | 0.37 |